code: def test_auto_rechunk():
      clf = ParallelPostFit(GradientBoostingClassifier())
      X, y = make_classification(
          n_samples=1000, n_features=20, chunks=100
      )
      X = X.rechunk({0: 100, 1: 10})
      clf.fit(X, y)
  
      assert clf.predict(X).compute().shape == (1000,)
      assert clf.predict_proba(X).compute().shape == (1000, 2)
      assert clf.score(X, y) == clf.score(X.compute(), y.compute())
  
      X, y = make_classification(
          n_samples=1000, n_features=20, chunks=100
      )
      X = X.rechunk({0: 100, 1: 10})
      X._chunks = (tuple(np.nan for _ in X.chunks[0]), X.chunks[1])
      clf.predict(X)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 11 for calling a global function with example usage: test_auto_rechunk() without return types